TY - GEN
T1 - Dynamic Changes in Brain Networks of Alzheimer's Disease Based on Co-Activation Patterns
AU - Wang, Mingjun
AU - Ma, Yunxiao
AU - Wu, Jinglong
AU - Funahashi, Shintaro
AU - Liu, Tiantian
AU - Yan, Tianyi
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The dynamic changes in brain networks during the progression from normal controls (NC) to mild cognitive impairment (MCI) and Alzheimer's Disease (AD) remain unclear. This study investigates the dynamic alterations in brain networks as the disease progresses from NC to MCI and ultimately to AD. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we performed the co-activation pattern (CAP) analysis on resting-state functional magnetic resonance imaging (rs-fMRI), focusing on the attentional network (AN), the default mode network (DMN), the frontoparietal network (FPN), the limbic network (LN), the motor-sensory network (MN), the salience network (SN), and the visual network (VN). The clustering analysis identified eight distinct CAPs, which are classified into three categories: primary sensory network (PSN) dominant, high-order cognitive network (HOCN) dominant, and PSN-HOCN co-dominant. We further analyzed CAP transition matrices and CAP occurrence rates across different disease groups. Results revealed that during the transition from NC to MCI, CAP transitions were primarily unidirectional and confined to the same CAP category, with increased dynamic activity of HOCN-dominant CAPs. In contrast, the transition from MCI to AD involved bidirectional CAP transitions with frequent inter-network transitions, while the AD stage exhibited complex interactions across multiple networks. These findings provide novel insights into the dynamic brain network changes during AD progression, potentially paving the way for future non-invasive brain stimulation therapies based on brain functional dynamics.
AB - The dynamic changes in brain networks during the progression from normal controls (NC) to mild cognitive impairment (MCI) and Alzheimer's Disease (AD) remain unclear. This study investigates the dynamic alterations in brain networks as the disease progresses from NC to MCI and ultimately to AD. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we performed the co-activation pattern (CAP) analysis on resting-state functional magnetic resonance imaging (rs-fMRI), focusing on the attentional network (AN), the default mode network (DMN), the frontoparietal network (FPN), the limbic network (LN), the motor-sensory network (MN), the salience network (SN), and the visual network (VN). The clustering analysis identified eight distinct CAPs, which are classified into three categories: primary sensory network (PSN) dominant, high-order cognitive network (HOCN) dominant, and PSN-HOCN co-dominant. We further analyzed CAP transition matrices and CAP occurrence rates across different disease groups. Results revealed that during the transition from NC to MCI, CAP transitions were primarily unidirectional and confined to the same CAP category, with increased dynamic activity of HOCN-dominant CAPs. In contrast, the transition from MCI to AD involved bidirectional CAP transitions with frequent inter-network transitions, while the AD stage exhibited complex interactions across multiple networks. These findings provide novel insights into the dynamic brain network changes during AD progression, potentially paving the way for future non-invasive brain stimulation therapies based on brain functional dynamics.
KW - co-activation pattern
KW - individual brain network parcellation
KW - mild cognitive impairment
KW - resting-state functional magnetic resonance imaging
KW - transitions
UR - http://www.scopus.com/inward/record.url?scp=105001013566&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI64163.2024.10906206
DO - 10.1109/CISP-BMEI64163.2024.10906206
M3 - Conference contribution
AN - SCOPUS:105001013566
T3 - Proceedings - 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024
BT - Proceedings - 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024
A2 - Li, Qingli
A2 - Wang, Yan
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024
Y2 - 26 October 2024 through 28 October 2024
ER -